The present invention relates generally to estimating longitudinal and lateral velocities of a motor vehicle and more particularly to dynamically estimating longitudinal and lateral velocities.
In recent years, there has been a tremendous increase in interest in advanced safety features in motor vehicles. This has led to the development of advanced vehicle chassis control systems, such as anti-lock brakes (ABS), traction control (TC), four-wheel steering (4WS), electronic stability program (ESP) to name but a few. To control a motor vehicle""s motion it is necessary for the control system to know the vehicle""s dynamic status, in terms of longitudinal velocity and acceleration, lateral velocity and acceleration, yaw rate, wheel speeds, and other parameters.
Information of all the dynamic signals can be obtained from sensor measurements. However, for an accurate estimate of the vehicle""s parameters, the number of sensors needed is quite large and the sensors are expensive. A large number of expensive sensors add unwanted cost and weight to the motor vehicle. The sensors also occupy valuable packaging space on the vehicle.
Various algorithms have been proposed for estimating the vehicle dynamics. A typical method uses linear techniques, such as Kalman filtering. However, this approach has limited success because of the inherent non-linearity of vehicle dynamics. Other estimation methods depend heavily on the accuracy of a model for tire dynamics as well as information from a road/tire friction coefficient. The computing power required in such detailed models easily exceeds the computing power available in a normal vehicle engine control unit (ECU).
There is a need for a robust velocity estimation method having relatively low computing power requirements and at the same time providing accurate vehicle dynamic estimations.
The present invention is a system and method for dynamically estimating the longitudinal and lateral velocities of a motor vehicle. It presents a robust velocity estimation method having low computing power requirements and therefore fits well within the technological and financial constraints for developing vehicle control systems. The present invention accesses the information of vehicle dynamic signals using a minimum number of low-cost, off-the-shelf sensors that measure longitudinal acceleration, lateral acceleration, wheel speed and yaw rate. The present invention provides a gain scheduled linear-parameter-varying (LPV) state observer for vehicle longitudinal and lateral velocities based on information gathered from just a few sensors.
It is an object of the present invention to model a vehicle""s dynamic behavior. It is another object of the present invention to estimate longitudinal and lateral velocities in linear and non-linear ranges of a vehicle""s motion.
It is a further object of the present invention to accurately model the vehicle""s dynamic behavior using low computing power from the vehicle""s microprocessor. It is still a further object of the present invention to provide a robust model of the vehicle""s dynamics that is independent of other vehicle parameters such as vehicle mass, center of gravity, moment of inertia and tire cornering stiffness.
Other objects and advantages of the present invention will become apparent upon reading the following detailed description and appended claims, and upon reference to the accompanying drawings.